def _update_time(input_time: Coord, advected_cube: Cube,
timestep: timedelta) -> None:
"""Increment validity time on the advected cube
Args:
input_time:
Time coordinate from source cube
advected_cube:
Cube containing advected data (modified in place)
timestep:
Time difference between the advected output and the source
"""
original_datetime = next(input_time.cells())[0]
new_datetime = original_datetime + timestep
new_time = input_time.units.date2num(new_datetime)
time_coord_name = "time"
time_coord_spec = TIME_COORDS[time_coord_name]
time_coord = advected_cube.coord(time_coord_name)
time_coord.points = new_time
time_coord.convert_units(time_coord_spec.units)
time_coord.points = round_close(time_coord.points,
dtype=time_coord_spec.dtype)
def _calculate_forecast_period(
time_coord: Coord,
frt_coord: Coord,
dim_coord: bool = False,
coord_spec: TimeSpec = TIME_COORDS["forecast_period"],
) -> Coord:
"""
Calculate a forecast period from existing time and forecast reference
time coordinates.
Args:
time_coord:
Time coordinate
frt_coord:
Forecast reference coordinate
dim_coord:
If true, create an iris.coords.DimCoord instance. Default is to
create an iris.coords.AuxCoord.
coord_spec:
Specification of units and dtype for the forecast_period
coordinate.
Returns:
Forecast period coordinate corresponding to the input times and
forecast reference times specified
Warns:
UserWarning: If any calculated forecast periods are negative
"""
# use cell() access method to get datetime.datetime instances
time_points = np.array([c.point for c in time_coord.cells()])
forecast_reference_time_points = np.array([c.point for c in frt_coord.cells()])
required_lead_times = time_points - forecast_reference_time_points
required_lead_times = np.array([x.total_seconds() for x in required_lead_times])
if time_coord.bounds is not None:
time_bounds = np.array([c.bound for c in time_coord.cells()])
required_lead_time_bounds = time_bounds - forecast_reference_time_points
required_lead_time_bounds = np.array(
[[b.total_seconds() for b in x] for x in required_lead_time_bounds]
)
else:
required_lead_time_bounds = None
coord_type = DimCoord if dim_coord else AuxCoord
result_coord = coord_type(
required_lead_times,
standard_name="forecast_period",
bounds=required_lead_time_bounds,
units="seconds",
)
result_coord.convert_units(coord_spec.units)
if coord_spec.dtype not in FLOAT_TYPES:
result_coord.points = round_close(result_coord.points)
if result_coord.bounds is not None:
result_coord.bounds = round_close(result_coord.bounds)
result_coord.points = result_coord.points.astype(coord_spec.dtype)
if result_coord.bounds is not None:
result_coord.bounds = result_coord.bounds.astype(coord_spec.dtype)
if np.any(result_coord.points < 0):
msg = (
"The values for the time {} and "
"forecast_reference_time {} coordinates from the "
"input cube have produced negative values for the "
"forecast_period. A forecast does not generate "
"values in the past."
).format(time_coord.points, frt_coord.points)
warnings.warn(msg)
return result_coord
